Testing and Improving an NLP/LLM-AI Tool for Verifying Naming in PASS Diagrams
Initial Situation: The subject-oriented process modeling language PASS is a simple, yet formal and especially executable modeling language. Due to its subject-oriented nature, the naming of different elements in PASS models is of particular importance for understanding. E.g., subject names should denote their active nature, while states in behavior diagrams should ideally be written to describe the activity as active verbs. In a previous work, a prototypical tool has been developed that checks a PASS model and informs a process modeler whether and how the naming in models could be improved. The tool works on a functional level but has not been tested thoroughly in regard to used underlying language model and employed prompts.
Goal: The goal of this thesis is to further develop the tool and test if or how different models behave with these tasks and if/how the results can be improved. E.g., by creating dedicated test scenarios/models containing typical possible mistakes in nameing.
Required Skills: Fundamental, object-oriented programming skills are necessary, and a willingness to learn.
Helpful Skills:
- Knowledge in (or willingness to learn) in C#
- Knowledge and experience in prompt engineering.